# Financial data analysis as an iterative feedback process

In the first few lectures in my Financial Econometrics course I emphasise that analysing financial data should be an iterative learning feedback process, where an initial idea may be modified after some statistical analysis reveals an incomplete or incorrect answer to our initial hypothesis.  It is important for my students to understand that econometrics is not about producing ‘black box’ results from statistical software but is a gradual learning pathway which leads to asking the correct question using the correct empirical design and data.  This enables the analyst to avoid what Kennedy called Type III errors; getting a statistically precise answer to the wrong question.

Here is a short video explaining this process:

# Stata Videos: 1-Introduction to Stata

This video is a gentle introduction to Stata, a data and statistical analysis package that I use in both my teaching and research.

# Model Parameter Stability

The first assumption of the classic linear regression model is that the dependent (or outcome) variable can be calculated as a linear function of a specific set of independent (or predictor) variables, plus a disturbance term1.  This statement implies that the unknown parameters of this linear function are stable or constant over the estimation period.   This assumption is particularly important when using a linear regression model to predict.

In practical terms you are assuming  the effect of your predictor(s) remains unchanged over the sample period; for relationships between financial variables this may be unrealistic in the presence of large ‘landscape changing’ events.  In econometric terms these large events are sometimes called break points.

This instructive video explains in more detail how to test for parameter stability, both when a break point is known and when we cannot clearly identify a break point

Click here for the do file, and here for a copy of the slides.

1. See Chapter 3 of Kennedy 1998 “A Guide to Econometrics” for an excellent introduction to the five assumptions underpinning classic linear regression models